Outlier detection with optimal hybrid deep learning enabled intrusion detection system for ubiquitous and smart environment

Ragab, Mahmoud; Sabir, Maha Farouk S.

doi:10.1016/j.seta.2022.102311

Cited by 16 publications

(15 citation statements)

References 13 publications

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“…It even leads to failure to manufacture some parts [45]. The island scanning strategy proved to be a helpful method for reducing residual stress due to the decreased scanning length [6]. However, an overlap region between the islands ensured the close metallurgical bonding of the materials between the islands.…”

Section: Overlap Ratementioning

confidence: 99%

“…Besides, in AM technologies, especially SLM printing, energy consumption is higher than in traditional manufacturing processes such as casting [4]. High energy consumption in SLM printing is not desired for modern manufacturing when green technologies are getting more attention [5,6]. Therefore, it is also essential to properly select manufacturing parameters such as LASER power to minimize power consumption.…”

Section: Introductionmentioning

confidence: 99%

“…Solid components, honeycomb structures, porous structures, thin-walled parts, and other types of parts can be made with SLM in a variety of materials, including titanium alloys, steel, and nickel alloys [5][6][7]. Aluminum alloys have sparked much interest in the SLM technique.…”

Section: Introduction Of the Slm Printer And Datasetsmentioning

confidence: 99%

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Selective LASER melting part quality prediction and energy consumption optimization

Sabuj

Afshari

Liang

2023

Meas. Sci. Technol.

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Selective LASER Melting (SLM) popularity is increasing because of its ability to quickly produce components with acceptable quality. The SLM process parameters, such as LASER power and scan speed, play a significant role in assuring the quality of customized SLM products. Therefore, the process parameters must be tuned appropriately to achieve high-quality customized products. Most existing methods for adjusting the SLM’s parameters use multiple inputs and one or two outputs to develop a model for achieving their desired quality. However, the number of the model’s input and output parameters to be considered can be increased to achieve a more comprehensive model. Furthermore, energy consumption is also a factor that should be considered when adjusting input parameters. This paper presents a multi-inputs-multi-outputs (MIMO) artificial neural network (ANN) model to predict the SLM product qualities. We also try to combine training data from different sources to achieve a more general model that can be used in real applications by industries. The model inputs are LASER power, scan speed, overlap rate, and hatch distance. Moreover, four critical product quality measures: relative density, hardness, tensile strength, and porosity, are used as the model’s outputs. After finding a proper model, an energy optimization method is developed using the genetic algorithm in this paper. The objective of the optimization is to minimize the energy consumption of SLM manufacturing with a less compromised output quality. The results of this study can be used in the industry to decrease energy consumption while maintaining the required quality.

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Section: Overlap Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Selective LASER melting part quality prediction and energy consumption optimization

Sabuj

Afshari

Liang

2023

Meas. Sci. Technol.

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“…Different security strategies and technologies have been developed to protect network security. Intrusion detection is a traditional network security technique [5][6][7]. An earlier intrusion detection system (IDS) mainly utilizes misuse detection.…”

Section: Introductionmentioning

confidence: 99%

Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for Clustered IoT Driven Ubiquitous Computing System

Salama¹,

Ragab²

2023

Computer Systems Science and Engineering

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In the Internet of Things (IoT) based system, the multi-level client's requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems (UCS). The UCS necessitates heterogeneity, management level, and data transmission for distributed users. Simultaneously, security remains a major issue in the IoT-driven UCS. Besides, energy-limited IoT devices need an effective clustering strategy for optimal energy utilization. The recent developments of explainable artificial intelligence (XAI) concepts can be employed to effectively design intrusion detection systems (IDS) for accomplishing security in UCS. In this view, this study designs a novel Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for IoT Driven Ubiquitous Computing System (BXAI-IDCUCS) model. The major intention of the BXAI-IDCUCS model is to accomplish energy efficacy and security in the IoT environment. The BXAI-IDCUCS model initially clusters the IoT nodes using an energy-aware duck swarm optimization (EADSO) algorithm to accomplish this. Besides, deep neural network (DNN) is employed for detecting and classifying intrusions in the IoT network. Lastly, blockchain technology is exploited for secure inter-cluster data transmission processes. To ensure the productive performance of the BXAI-IDCUCS model, a comprehensive experimentation study is applied, and the outcomes are assessed under different aspects. The comparison study emphasized the superiority of the BXAI-IDCUCS model over the current state-of-the-art approaches with a packet delivery ratio of 99.29%, a packet loss rate of 0.71%, a throughput of 92.95 Mbps, energy consumption of 0.0891 mJ, a lifetime of 3529 rounds, and accuracy of 99.38%.

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“…So, it is very essential tibuid a network with high security [7], [8]. So, a highly defensive modern network with new security requirements is essential to overcome the network from such attacks [9], [10].…”

mentioning

confidence: 99%

Multi level trust calculation with improved ant colony optimization for improving quality of service in wireless sensor network

Ahmed

Abbas

Rashid

2023

IJ-AI

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Wireless sensor network (WSN) is the most integral parts of current technology which are used for the real time applications. The major drawbacks in currect technologies are threads due to the creation of false trust values and data congestion. Maximum of the concept of WSNs primarily needs security and optimization. So, we are in the desire to develop a new model which is highly secured and localized. In this paper, we introduced a novel approach namely multi level trust calculation with improved ant colony optimization (MLT-IACO). This approach mainly sub-divided into two sections they are multi level trust calculation which is the combination three levels of trust such as direct trust, indirect trust and random repeat trust. Secondly, improved ant colony optimization technique is used to find the optimal path in the network. By transmitting the data in the optimal path, the congestion and delay of the network is reduced which leads to increase the efficiency. The outcome values are comparatively analyzed based the parameters such as packet delivery ratio, network throughput and average latency. While compared with the earlier research our MLT-IACO approach produce high packet delivery ratio and throughput as well as lower latency and routing overhead.

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Outlier detection with optimal hybrid deep learning enabled intrusion detection system for ubiquitous and smart environment

Cited by 16 publications

References 13 publications

Selective LASER melting part quality prediction and energy consumption optimization

Selective LASER melting part quality prediction and energy consumption optimization

Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for Clustered IoT Driven Ubiquitous Computing System

Multi level trust calculation with improved ant colony optimization for improving quality of service in wireless sensor network

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